Echocardiography guided management of atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: The growing burden of atrial fibrillation (AF) experienced by the general population translates into an increased incidence in the intensive care setting, further aggravated by illness severity. New onset AF has been established as an independent mortality predictor. Cardiology management guidelines are based on major trials that included ambulatory patients with varying degrees of ventricular systolic and diastolic dysfunction, and with variable dependences of left ventricular filling on atrial systole. Emphasis is placed on rate control combined with anticoagulation therapy, along with careful consideration of limiting any myocardial depression by antiarrhythmic medication in patients who often already have some form of structural heart disease. DESIGN: Narrative review Objectives: Critical care echocardiography (CCE) is well established as a widely available diagnostic and monitoring tool in haemodynamically unstable patients. It assists in identifying risk factors associated with arrhythmias, reveals parameters associated with arrhythmia chronicity, and guides therapy to facilitate a return to sinus rhythm. CCE helps guide the crucial management decision to seek either rhythm or rate control and, with rhythm control, monitors return of mechanical sinus rhythm with left atrial recovery post cardioversion. Echocardiography can also help when conflicting management goals are present, such as guideline-driven therapeutic anticoagulation in the intensive care patient that is at significant risk of bleeding. RESULTS: This review seeks to assist intensive care practitioners managing patients with AF, with a focus on the many benefits CCE offers, blending specific intensive care medicine data to current cardiology guidelines on arrhythmia management in these severely ill patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".